Methods and Systems for Analyzing and Visualizing Spray Patterns

ABSTRACT

Computer-implemented systems and methods predict behavior of sprays based on receiving a selection of one or more variables affecting spray. Relative amounts of the droplets forming the spray are grouped into various droplet size classes, where each droplet size class represents a range of droplet sizes. The relative amounts of the spray in the classes is visually depicted on a computer display according to a distribution of droplets, a volume of spray falling within the droplet size classes, a chart depicting relative amounts of the spray as a function of droplet size, or according to a spray quality based on environmental factors.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.14/755,500, filed on Jun. 30, 2015, which is a continuation of U.S.patent application Ser. No. 13/734,571, filed on Jan. 4, 2013, andissued as U.S. Pat. No. 9,098,732, the entire contents of each of whichare hereby incorporated by reference for all purposes.

FIELD OF TECHNOLOGY

Methods and systems analyze and graphically display spray patterns basedon user selections. More specifically, computer-implemented approachesenable users to observe differences in various spray patterns used inagricultural treatments based on the user's selections of sprayvariables.

BACKGROUND

Due to increasing concern about pest control costs and environmentalpollution associated with agricultural sprays, application of suchsprays requires precision and care. Considerable research on spray drifthas been conducted, but it remains a major problem associated with manyagricultural spray applications. Even when test data, for instancecharacterizing the drift potential or leaf coverage of an agriculturalspray, are available, this information is difficult to communicate toindividuals in systematic and easily understandable terms. Typically,spray patterns of agricultural sprays, such as pesticides, must betested in order to provide individuals with desired result data; orwhere previously analyzed results are available, the information isrequired to be added to a custom presentation or report for theindividual. In addition, spray patterns are affected by the type ofnozzle used to deliver the spray, and nozzles must be tested or nozzleanalysis results are required to be added to custom presentations.Further, other variables affecting spray such as environmental factorsmay not be available. Conducting these processes is time-consuming, testresults may be incomplete due to unavailable information, and theresults may not be delivered in a timely manner.

SUMMARY

In view of the foregoing, there is a need to provide an approach thatrapidly delivers meaningful agricultural spray test data to users.Further, there is a need to provide an approach that allows users toselect variables affecting spray patterns in order to understand andcompare predicted spray patterns based on one or multiple agriculturaltreatments of interest.

The present disclosure, therefore, provides computer-implementedapproaches that generate and display agricultural spray patterninformation. This spray information displayed may be based on sprayanalyses, such as sprays analyzed using laser diffraction analysis.Users may enter selections including variables affecting a spray patternsuch as composition, spray conditions and environmental factors, and adisplay may provide visual information about the analyzed spray pattern,its quality or acceptability.

In some aspects, a computer-implemented method for depictingagricultural spray behavior as a spray distribution involves using acomputer processor, which receives selections of an agricultural sprayand parameters at which the agricultural spray is to be sprayed. Theprocessor retrieves analyzed spray particulate data based on theselections, which includes a distribution of relative amounts ofagricultural spray droplets within droplet size classes, where eachclass corresponds to a range of droplet sizes. A computer displaygraphically displays the distribution of the relative amounts of thespray droplets in the droplet size classes and depicts the spraydroplets as a series of representative droplets, where eachrepresentative droplet is associated with one of the droplet sizeclasses. The representative droplets are arranged within a distributioncurve representing a distribution of size of the representative dropletsbased on the relative amounts, thereby providing a visual display of adistribution of the droplet size of the selected sprayed fluid.

In other aspects, a computer-implemented method for depictingagricultural spray behavior involves using a computer processor, whichreceives selections of an agricultural spray and one or more parametersat which the agricultural spray is to be sprayed, and in response,retrieves analyzed spray particulate analysis data including relativeamounts of agricultural spray droplets within droplet size classescorresponding to a range of droplet sizes. A computer displaygraphically displays the relative amounts of the spray droplets in thedroplet size classes.

In further aspects, a computer-implemented method for providingagricultural spray information involves using a computer processor,which analyzes spray particulate data of sprayed agricultural fluids toidentify a droplet size distribution of the sprayed fluids; groupsdroplets within the droplet size distribution into droplet size classes,where each droplet size class represents a range of droplet sizes;calculates a relative amount of the droplets within the droplet sizeclasses; and receives a selection of an agricultural mixturecorresponding to one of the sprayed fluids. A computer display of thecalculated relative amounts of the droplets within the droplet sizeclasses for the spray particulate data is displayed based on thereceived selection.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of a method for providing spray behaviorinformation in a visual format.

FIG. 2 depicts a user interface providing user-selectable fields forselection of spray parameters to be used in providing spray behaviorinformation in a visual format.

FIG. 3A depicts a user interface for providing a distribution view of afirst spray.

FIG. 3B depicts a user interface for providing a distribution view of asecond spray.

FIG. 4 depicts a user interface for providing a volume view of a firstand second spray.

FIG. 5 depicts a user interface for providing a cumulative distributionchart of a first and second spray as a function of particle size versuscumulative distribution.

FIG. 6 depicts a user interface for providing a spray view of a spray.

FIG. 7 is a block diagram of a computer system providing a sprayvisualization tool according to certain implementations.

FIG. 8 depicts a visual representation of spray droplets on anagricultural target

DETAILED DESCRIPTION

Computer-implemented approaches provide spray visualization tools thatenable users to select variables affecting spray patterns, such as foragricultural sprays, and view differences in sprays based on theseselections. The disclosed approaches are useful in delivering sprayanalysis data in a user-friendly, visual format, which may educate usersabout predicted spray patterns according to spray parameters of interestand may allow users to refine spray parameters of interest basedthereon. These implementations may additionally include informationrelated to spray drift (e.g., off-target movement) due to wind speed andleaf coverage due to boom height. This may enable users to assesswhether sprays will be effective for treating crops in certainenvironmental conditions.

FIG. 1 illustrates a flow diagram of a method 100 for providing sprayinformation in a visual format, which may be useful in understanding thebehavior of sprays composed of various components, such as agriculturalcomponents used in treating crops and soil. The method 100 may beimplemented using the computer system of FIG. 7.

Method 100 may involve executing instructions using a computer processorfor analyzing 110 spray particulate data of sprayed fluids to identify adroplet size distribution of the droplets defining the sprayed fluids.Analyzing spray particulate data 110 may involve receiving data from ananalysis device configured to evaluate a sprayed fluid. For example,spray analysis methods may include laser diffraction analysis within aclosed system, such as a wind tunnel spray analysis device.

The analyzed 110 spray particulate data may include a range of dropletsizes within the spray distribution. In addition, informationidentifying the analyzed sprayed mixture and additional variables thataffect how the mixture is sprayed may be provided. This information mayinclude: spray identification information, such as compositionparameters, of the mixture including active ingredients and adjuvants;and additional spray parameters beyond the spray composition, such asdelivery parameters, including active ingredient rates, adjuvants rates,spray pressure, rate of spray per acre (e.g., spray volume per acre),spray pressure (e.g., 20 psi, 40 psi), and nozzle type (e.g., XR11002,XR11003, and AIXR11002), as well as environmental parameters affectingspray, such as boom height and wind speed.

With respect to the aforementioned delivery parameters affecting spray,when the spray is analyzed using a fluid delivery system, includingclosed systems such as wind tunnels, these delivery parameters may becontrolled and/or monitored during testing. For example, spray pressuremay be monitored using the fluid delivery system and variations inpressure may be recorded to confirm that spray analysis is recordedwhile the spray is delivered at a selected pressure, which may ensureaccurate spray behavior analysis information is documented. In anotherexample, for mixtures sprayed through a nozzle, the spray produced fromthe mixture may be affected by the nozzle type as well as thecomposition in the mixture, e.g., pesticides and adjuvants, and thesevariables may be recorded during analysis. In some cases, the analyzedfluid may be water, such as when water is used as a baseline forcomparison with agricultural sprays formed of active ingredients.

With respect to environmental parameters, such as boom height in groundspray applications, the boom height may be set close to the ground(e.g., 18 inches from the crop canopy) or at a slight elevation (e.g.,36 inches from the crop canopy), and these variations may affect whethera spray reaches its target (e.g., reaches leaves of agricultural crops)or whether the spray is at risk of drifting off-target. Wind speed mayadditionally affect whether the spray reaches its target. In someaspects, these environmental parameters affecting spray may be based onfield studies and modeling, which may be in addition to other modes ofspray analysis.

Method 100 continues by grouping 120 the analyzed spray particulateswithin the distribution into droplet size classes. Each of the sizeclasses may represent a range of droplet sizes. The droplet size classesmay be defined upon receiving the spray particulate data, or the dropletsize classes may be predefined, for example, based on droplet sizes thatmay be at risk for drift, that may traditionally reach the intendedtarget, and that may contribute to leaf runoff. In addition, thepredefined droplet size classes, and the ranges for acceptable versusunacceptable droplet sizes, may differ based on the type of agriculturalspray. Where a variety of size classes are available, one or more sizeclasses may be selected based on the composition of the sprayed fluid,spray parameters and combinations thereof.

In one example, the droplet size classes may be divided into sizeranges, such as, discrete ranges based on a volume median diameter(“VMD”) of less than 136 μm, from 136 μm to 177 μm, from 178 μm to 218μm, from 219 μm to 349 μm, from 350 μm to 428 μm, from 429 μm to 622 μm,and greater than 622 μm. The VMD is known as D_(V0.50) or X₅₀ and istypically characterized in micron units (μm). The VMD numeric value isthe median droplet size of the spray such that half of the volume of thespray contains droplets smaller than the VMD and half of the volumecontains droplets larger than the VMD. A smaller VMD may correspond to afine spray and a larger VMD may correspond to a coarser spray.

In some aspects, the droplet size classes may be defined according to aspray's statistical moment, such as the median size (e.g., X₅₀) or X₁₀(e.g., where 10 percent of the volume of spray is in droplets smallerthan this value).

In additional aspects, the droplet size classes may be associated with avolume of the spray within a size class. For example, the percent ofspray volume<105 um (V105) is defined as driftable fines by ASTM and maybe valuable in characterizing the drift potential of a spray, and twoclasses may be defined for droplets falling below <105 μm (V105) anddroplets falling above this value.

The droplet size classes may additionally or alternatively becharacterized by droplets per in² (“dpi²”) at a given application rate(such as 10 gallons per acre or GPA) such as greater than 4528 dpi²,2078 dpi², 1112 dpi², 271 dpi², 147 dpi², 48 dpi², and less than 48dpi². In some examples, a VMD of the droplets may be used to approximatethe dpi² value. In addition, the dpi² value may describe an upper limitof a droplet size class. In some aspects, the dpi² of a given spray maybe depicted visually. For example, in FIG. 8, representative dropletsfor four different sprays 801-804 spray are superimposed over a weedleaf image 805 based on the maximum per-area leaf coverage (e.g., dpi²)at their respective median particle size and carrier volume rate (e.g.,gallons per acre). The median particle sizes may be retrieved from thecomputer system of FIG. 7 and the dpi² provided visually for each spraycombination (e.g., for the combination of variables that affect a spray:active ingredients, adjuvants, spray pressure, carrier volume rate, andnozzle type). As shown in FIG. 8, while the volume of spray per squareinch of leaf is the same for the four sprays, leaf coverage differs withdiffering dpi² levels. The visual representation of the spray may belinked to, or provided on, one or more of the user interfaces discussedbelow, or may be provided as a separate user interface. Although FIG. 8represents a weed leaf image, the visual representation may be for anyagricultural spray target such as a crop plant or crop plant leaf atvarious stages of growth. In addition, the dpi² visual representationmay be displayed on the agricultural target on a per spray basis, or maybe displayed side-by-side for comparison of sprays. Further, in additionor as an alternative to displaying the spray on a dpi² basis, the spraymay be displayed on a VMD or another droplet size class basis.

The size classes may additionally or alternatively be assigned sprayqualities such as very fine (“VF”), fine (“F”), medium (“M”), course(“C”), very course (“VC”), extra course (“XC”) and ultra course (“UC”).For example, the spray qualities may be based on droplet sizeclassifications used in the industry, such as Spraying Systems TeeJetTechnology Catalog 51. The spray qualities may be color-coded by theASABE S572.1 test method. In some applications, the spray qualities maybe associated with one or more of the VMD ranges. Further, the classesmay be assigned a drift potential rating such as from high to low driftpotential.

In some implementations, prior to grouping 120 the analyzed sprayparticulates into classes, the overall particulate count may be reduced.For example, the count of droplets may be represented as one hundredmillionth (1×10⁻⁸) of the droplets present in 10 gallons of liquid.

In method 100, the relative amount of the spray within the droplet sizeclasses may be calculated 130, which may identify an overalldistribution of the spray particulates within the spray. Calculatingrelative amounts of spray may involve one or both of calculating avolume of the spray within the classes or calculating a count ofdroplets within the classes. For example, a percentage of the sprayvolume or a percentage of spray droplets falling within the droplet sizeclasses of the present disclosure for a variety of sprays may becalculated 130. In one example, the percent spray volume falling belowand above 105 μm may be calculated for various sprays, which mayidentify the percentage of driftable fines within such sprays.

In some aspects, the droplet sizes, droplet size classes and calculatedrelative amounts of the droplets, droplets and volume within theclasses, other spray analysis information, and combinations andvariations thereof may be stored in a database, such as the database ofthe computer system of FIG. 7.

Method 100 continues by receiving 140 selections identifying the sprayedfluids associated with the spray particulate data. The selection may bea user selection of one or more variables affecting spray such as spraycomposition parameters, including active ingredients and adjuvants; andspray parameters including delivery parameters, such as spray pressure,carrier volume rate (e.g., gallons per acre (“GPA” such as 10 GPA),product use rate (e.g., pesticide use rate, adjuvant use rate, orcombinations), nozzle type, and environmental parameters, such as boomheight and wind speed. For example, the selection may be one or more ofan active ingredient and an adjuvant along with a selection of one ormore of a rate of spray (e.g., carrier volume), a nozzle type and aspray pressure. In a further example, the selection may include one ormore of a wind speed or boom height at which the spray is delivered. Insome implementations, the received selection may be a user selectionobtained from one or more user interfaces, such as from the userinterfaces illustrated in FIGS. 2-6 of the present disclosure.

Based on the received selection 140, the calculated relative amounts ofthe spray within the droplet size classes for the spray particulate datamay be retrieved from a database and displayed 150 on a computerdisplay. For example the spray may be depicted as spray dropletsrepresenting droplet size ranges or may be depicted as a spray volume ofthe spray droplets falling within the droplet size ranges. Particularly,the spray characteristics may be depicted using a variety of displaytypes, such as via the user interfaces illustrated in FIGS. 3A-6 of thepresent disclosure. In aspects where the overall particulate count isreduced, the full count population of the droplet reduction may bedepicted on the computer display.

Turning to FIG. 2, a user interface 200 displays user-selectable fieldsfor entering spray variables used in providing spray analysis results ina visual format. The selectable fields in FIG. 2 include a carriervolume 210 corresponding to a spray volume per acre, such as gallons peracre; an active ingredient 220 corresponding to crop treatment chemicals(e.g., active ingredients) such as one or more pesticides; and anadjuvant 230 corresponding to one or more surfactants, crop oilconcentrates, nitrogen fertilizers, spreader-stickers, wetting agents,penetrants and so on. The spray mixture selection depicted in FIG. 2represents a first selected mixture, and a first user-selectable area“Mix 1” 240 enables the user to select and view the spray variables forthe first mixture. A user may toggle between spray selections foranother mixture “Mix 2” by selecting a second user-selectable area “Mix2” 250. A user interface for “Mix 2” may be substantially similar to theuser interface 200 of FIG. 2, thereby enabling a user to select twodifferent sprays for comparison. In addition, the user may addselections for “Mix 1” or “Mix 2” as a preferred or favorite spray byselecting the plus icon 260, and may recall the added selection byselecting the star icon 270. Although FIG. 2 provides three selectablefields for identifying a spray variables for a mixture to be analyzed,the user interface 200 may provide more or less fields for suchidentification, and the fields may correspond to any of the variablesaffecting spray according to the present disclosure. In addition oralternatively, constant values may be assigned to one or more variablesaffecting spray. In use, once a user has entered mixture selections fora spray, or two or more sprays for comparison, the analyze icon 280 maybe selected for review of a spray analysis of the selected spray, or fora comparison of the analysis for the selected sprays.

FIG. 3A depicts a user interface 300 providing a distribution view 301of a selected spray illustrating how spray variables affect spraybehavior with respect to the spray's distribution. The distribution view301 may be provided in connection with aspects of the method 100 of FIG.1, and user selections of spray variables for displaying thedistribution view 301 may be received via the user interface 200 of FIG.2 as well as the user interface 300. The distribution view provides avisual depiction of a population of sprayed droplets based on a selectedspray, such as the first spray mixture “Mix 1” 302. The components ofthe spray mixture of “Mix 1” may be displayed, such as a mixture of apesticide at 22 oz. per acre at 10 gallons per acre. In some aspects,selections may be entered via user interface 300 related to the mode ofspray delivery of the first spray mixture, such as delivery from aparticular nozzle. For example, the nozzle field 305 may receive aselection identifying a nozzle type (e.g., XR11003, AI11005 or anothernozzle type) through which the spray will be delivered. The mode ofspray may be additionally or alternatively tailored using the pressurefield 310, which may receive selections identifying a specified pressure(e.g., 30 psi, 40 psi, or 50 psi,) at which the spray will be delivered.

In some aspects, selection of the analyze icon 315 following selectionof the spray variables results in displaying the distribution view 301.For example, the distribution view 301 in FIG. 3A is depicted as aseries of representative droplets 320 that may be generated bypredicting the relative amounts of the droplets within the droplet sizeclasses when the first mixture is sprayed using the selected nozzle atthe selected pressure. Each representative droplet displayed may beassociated with one of the droplet size classes of the presentdisclosure and may represent a predetermined volume of spray. For sprayshaving a large volume of spray falling within the droplet size class, alarger number of the representative droplets may be displayed comparedto another droplet size class having a relatively smaller volume ofspray.

In further aspects, the representative droplets within the droplet sizeclass may be displayed with droplet size information 325 indicative ofthe range of droplet sizes for the representative droplet such as rangeof droplet sizes represented, e.g., according to droplet diameter; driftpotential, e.g., where smaller droplets are at risk of particle drift;and leaf runoff potential, e.g., where large droplets are at risk ofbouncing and running off of leaves. The droplet size information 325 maybe displayed by selecting or “hovering” over the representative droplet.

The representative droplets 320 within the distribution view 301 may bearranged within a distribution curve 330 representing a distribution ofsize of the representative droplets based on the relative amounts of thedroplets within the droplet size classes. This may provide a visualdisplay of a distribution of the droplet size of the selected sprayedfluid. Further, within each size class, the size of the representativedroplets 320 may be the same, but across classes, the representativedroplet size may vary. This may further provide a user with a visualindication of the spray volume across the distribution curve 330 basedon droplet size class.

In addition, the user interface 300 may display a span value 335 of thedistribution of the spray. The span value 335 is a relative span of thespray:

(X₉₀−X₁₀)/X₅₀,

where X₉₀ indicates that 90 percent of the volume of spray is indroplets smaller (or 10 percent larger) than this value, X₁₀ indicatesthat 10 percent of the volume of spray is in droplets smaller than thisvalue, and X₅₀ is the volume median diameter of the spray. An example ofa span calculation is where X₅₀ is 200 μm, X₉₀ is 500 μm and X₁₀ is 260μm, giving a span of ([500−260]/200)=1.3. Generally, a relatively higherspan value represents a variable spray pattern, whereas a relativelylower span value represents a more consistent spray pattern. Forexample, a span value of about 1.5 may be characterized as highlyvariable, a span value of about 1.0 may be characterized as a consistentspray and a span value of less than about 1.2 may be characterized as anideal spray. Further, a VMD value 340 of the distribution may bedisplayed, and in FIG. 3A, the VMD value displayed is 179 am.

In some aspects, a value within the particulate size field 345 may beselected and the system may calculate and display a percentage of sprayvalue 350 for the portion of the spray droplets corresponding to theselected particulate size value. The particulate size field 345 mayprovide a variety of droplet sizes at which the system calculates thepercentage of volume of particulates falling at, above or below thedroplet sizes, or may provide a variety of droplet size ranges at whichthe percentage of particulates falling within the range may becalculated. In some aspects, the particulate size field 345 may providea cumulative volume percent of percent fines or V_(n), where the percentof spray volume is smaller than a given droplet size (n microns).Percent fines may be droplets smaller than 105 μm (e.g., based on ASTMTest Method E2798-11). For example, in FIG. 3A, the selected particulatesize value in the particulate size field 345 is particulates that aresmaller than 105 μm, and the percentage of spray value 350 containingdroplets smaller than 105 μm is 17.8 percent. Percent fines mayadditionally or alternatively be droplets smaller than 210 μm, asdroplets falling below this size may be considered subject to drift.

Using the various mix, analyze, spray and gallery icons 355, a user maytoggle between various user interfaces provided according to the presentdisclosure. By selecting the switch icon 360, the system may togglebetween distribution views of the selected spray mixtures. For example,as shown in FIG. 3B, the user interface 370 provides a distribution view371 of the sprayed droplets from the spray in the second spray mixture“Mix 2” 372, and the user interface 370 may be displayed in response toselecting the switch icon 360 in FIG. 3A.

A comparison of the distribution views 301, 371 of FIGS. 3A and 3B showsthe distribution of the particulates differ visually, and that the spanvalue 335 for the first mix is 1.3, whereas the span value 335 for thesecond mix is 1.1. In addition, the percentage of spray value 350containing droplets smaller than 105 jam is 17.8 percent for the firstmix, whereas this value is 0.6 percent for the second mix. Further, theVMD 340 for the first mix is 179 μm, while this value for the second mixis 513 μm. By comparing the distribution views 301, 371, relativedifferences between the sprays may be assessed, and based on theinformation displayed in FIGS. 3A and 3B, the second spray mixture “Mix2” 372 is associated with a more consistent spray with its relativelysmaller span value 335, a relatively higher VMD value 340 and lessdroplets with a size smaller than 105 μm.

Further, by receiving different selections for a spray or selecting fromone or both of the nozzle field 305 and the spray field 310, and byselecting the analyze icon 315, a new distribution view for the newselection may be displayed on the user interface 300, 370.

FIG. 4 depicts a user interface 400 illustrating a volume view 401 of afirst and second selected spray, “Mix 1” 402 and “Mix 2” 403. This spraybehavior view may be may be provided in connection with aspects of themethod 100 of FIG. 1, and selections for the two different sprays may bereceived via the user interface 200 of FIG. 2 for “Mix 1” 402, and via asecond user interface for “Mix 2” 403, described above, as well as viauser interface 400. The volume view 401 may provide a calculation of thespray volume for one or more sprays according to the droplet sizeclasses of the present disclosure. In some aspects, selections may bereceived for two different sprays, such as sprays with differing activeingredients, adjuvants, or both, and the relative amounts of thedroplets within the droplet size classes may be displayed as avolumetric comparison of the two different selections.

Using FIG. 4 as an example, the predicted spray falling within dropletsize classes produced by the “Mix 1” 402 components may be comparedagainst the “Mix 2” 403 components. The spray components may be based onselections such as the active ingredient, adjuvant and spray volumeselections from FIG. 2. The mixes 402, 403 in this example are formed ofdifferent components in which “Mix 1” is formed of a pesticide and “Mix2” is formed of the same pesticide along with an adjuvant, and the userinterface 400 receives selections of the same nozzle (e.g., XR11003) andpressure (e.g., 40 psi) via the selectable nozzle fields 405, 410 andthe selectable pressure fields 415, 420 for each of the mixes 402, 403.Based on the selections, the calculated relative amounts of the dropletsfalling within the droplet size classes for each of the mixes may bedisplayed on a per volumetric unit basis. In FIG. 4, the relativeamounts within each class are displayed on a per gallon basis based onreceiving a selection from the tank volume field 425 of 100 gallons. For“Mix 1” 402, the relative amounts of the spray within the classes 430are displayed above the droplet size class categories 435 (e.g., VF(very fine), F (fine), M (medium), C (course), VC (very course) and UC(ultra course)) proximate where “Mix 1” 402 is identified on the userinterface 400. Similarly, for “Mix 2” 403, the relative amounts of thespray within the classes 440 are displayed below the droplet size classcategories 435 proximate where “Mix 2” 403 is identified on the userinterface 400. Because the only difference between the mixes 402, 403and the conditions (e.g., nozzle type and pressure) at which the mixesare sprayed is the addition of the adjuvant in “Mix 2” 403, the volumeview 401 displays the effect of the adjuvant has on the sprayedpesticide based on droplet size class category.

In further aspects, the user interface 400 of FIG. 4 may be used tocompare sprays that are sprayed at different conditions. For example byreceiving mix selections or spray condition selections different fromthat depicted in FIG. 4, in combination with receiving a selection ofone or more of the spray icons 445, 450 for “Mix 1” 402 and “Mix 2” 403,respectively, the system may display a volume view for the newselection.

The user interface 400 of FIG. 4 may enable a user to determine whethera spray reaches its target, is lost (e.g., due to drift) or isineffective (e.g., due to droplets falling off of the leaves). Infurther aspects, a user may determine the cost-effectiveness of a spraybased on these determinations. For example, where the pricing of themixture is stored or where the pricing is entered by a user, thecost-effectiveness of the sprays may be assessed. In addition, based onthe volumetric results and depending on the type of active ingredient,e.g., pesticide, the system may characterize the sprays as acceptable orunacceptable or may rank the sprays relative to one another. Forinstance, some sprays such as those used in contact fungicideapplications have recommended spray grades of medium (M), fine (F) andvery fine (VF), and fungicide sprays with droplets falling within thesegrades may be characterized as acceptable. In contrast, forpost-emergence herbicides having recommended spray grades of medium (M),course (C) and very course (VC), the herbicide sprays having gradeswithin the fine and very fine grades may be characterized asunacceptable. Accordingly, aspects of the present disclosure may accountfor the different modes of action for these products, which mayfacilitate a user selecting variables affecting spray to achieve adesired level of leaf surface coverage or drift control.

FIG. 5 depicts a user interface 500 illustrating a cumulativedistribution chart 501 of a spray distribution for a first and secondspray, “Mix 1” 502 and “Mix 2” 503, as a function of particle size(x-axis) versus cumulative distribution (y-axis). The cumulativedistribution chart representing mixes 502, 503 may be provided inconnection with aspects of the method 100 of FIG. 1, and user selectionsof the mixes for displaying the distribution view 501 may be receivedvia the user interface 200 of FIG. 2 for “Mix 1” 502, via a second userinterface for “Mix 2” 503, described above, and via user interface 500.For example, in FIG. 5, the spray conditions may be received via theuser interface 500 in the same manner described above in connection withentry of spray conditions via the user interfaces 300, 370, and 400 ofFIGS. 3A, 3B and 4. Based on receiving two different spray variableselections, the relative amounts of the droplets within the droplet sizeclasses are used to generate a cumulative distribution chart 501 foreach of the mixes 502, 503 for simultaneous display. In someimplementations, the charts are generated based on data points collectedthrough the spray analysis of the present disclosure. The cumulativedistribution chart 501 enables a user to make comparisons of spraysbased on their compositions, nozzles and pressures generating the spray,as well as other variables affecting spray. In some aspects, thecumulative distribution chart 501 displays droplet information 504 aboutthe relative amounts of the spray falling within a certain size range ordroplet size class. For example, in FIG. 5, droplet informationdisplayed indicates that droplets that are smaller than 105 μm make up24.03 percent of “Mix 1” and 0.57 percent of “Mix 2.” In furtheraspects, droplet information 504 may be displayed by selecting or“hovering” over the representative droplet. For example, where multipledata points form the lines for the mixes 502, 503, in the distributionchart 501, the data points may be associated with droplet size classes,and by selecting one of the data points, droplet information 504 may bedisplayed in the manner shown in FIG. 5, or additionally oralternatively, in the manner shown in FIG. 3A in connection with thedisplayed droplet size information 325.

In addition, the spray variables may be updated based on spray iconselections as described above in connection with FIGS. 3A, 3B and 4, andthe user interface 500 may display updated results in response to theselection.

FIG. 6 depicts a user interface 600 showing a spray view 601 of aselected spray, “Mix 1” 602. The spray view 601 may be provided inconnection with aspects of the method 100 of FIG. 1, and user selectionsof spray variables for displaying the spray view 601 may be received viathe user interface 200 of FIG. 2 as well as via the user interface 600.The spray view 601 provides a qualitative description of the potentialfate of a spray, based, in part, on literature values for environmentalfactors that may not be accounted for using a closed system. Forexample, in FIG. 6, these environmental factors include boom height andwind speed, and these variables may be selected using the boom heightfield 605 and the wind speed field 610. The boom height field 605enables selections related to a distance from the ground or the cropcanopy at which the spray is delivered. For example, the spray may bedelivered at about 18 or 36 inches from the crop canopy, where 18 inchesmay be a low boom height available for selection in the boom heightfield 605 and 36 inches may be a high boom height available forselection in the boom height field 605. Alternatively, the spray may bedelivered to soil or small plants close to the soil and a low boomheight selection may represent only a slight elevation from soil,whereas a high boom height selection may represent a relatively higherelevation from the soil. The wind speed field 610 provides selectionsrelated to a variety of wind speeds the spray may encounter duringapplication to soil or foliage. For example, the selections may includerelatively low wind speeds of 5 miles per hour, or relatively high windspeeds of 10 miles per hour. In contrast to the distribution, volumetricand chart views depicted in FIGS. 3A to 5, in which the sprays areanalyzed in a closed system, the results of the analyzed spray displayedin the spray view 601 of FIG. 6 are further based on field studies andmodeling.

The spray analysis results depicted in FIG. 6 thus provide informationabout the fate of the sprayed droplets, and may be characterized basedon the predicted droplet response as a function of the composition ofthe spray, delivery parameters, and one or more environmentalparameters. The predicted response areas in FIG. 6 are based on aqualitative description of the droplet fate and include droplet runoffpotential 615, on-target potential 620, drift potential 625 andevaporation potential 630. For “Mix 1” 602, the droplet response of thespray is characterized as having a leaf runoff potential of low 635; anon-target potential 620 of moderate 640; a drift potential 625 of high645; and an evaporation potential of moderate 650. The relative amountsof the spray falling within the predicted response areas may bedetermined based on the calculated percent of the spray volume fallingwithin the droplet size categories of the present disclosure. Forexample, droplets with a high potential for leaf runoff may have a sizeof greater than 550 μm, and this may be true at all wind speeds and boomheights. Droplets with a high potential for on-target application mayhave a size range of between about 200 μm to about 550 μm, however,higher wind speeds and boom heights may decrease the potential tomoderate or low. Droplets with a high potential for drift may bedroplets having a size range from about 50 μm to about 200 μm, and highwind speeds and high boom heights may increase the potential for drift.Droplets with a high potential for evaporation may have a size of lessthan 50 μm, and this may be true at all wind speeds and boom heights.

The user interface 600 enables users to view the various spray mixtures,allows updating of sprays via both user interface 600 and user interface200, and enables toggling between spray mixes upon selection of theswitch icon 655.

The spray analysis information of the present disclosure may bedisplayed on a computer screen, such as a screen coupled to a PC, amobile phone, a tablet and so on. Users may enter selections via theuser interfaces (e.g., via pull down menus, radio buttons, free textfields and so on) and view the results via the screen. In some aspects,the users may access the spray analysis results using a tablet computer,for example, via a mobile software application. In addition, the systemmay be modified or updated, for example, based on EPA spray driftregulatory information and leaf coverage information. By providingagricultural spray results in a visually understandable format, theresults may be evaluated by the users to understand whether the spraysare acceptable for reaching the intended target or whether the sprayscontribute to spray drift, leaf runoff or evaporation.

In the present disclosure, the methods disclosed may be implemented assets of instructions or software readable by a device. Further, thespecific order or hierarchy of steps in the methods disclosed areexamples of sample approaches and the specific order or hierarchy ofsteps in the method can be rearranged while remaining within thedisclosed subject matter. The accompanying method claims presentelements of the various steps in a sample order, and are not necessarilymeant to be limited to the specific order or hierarchy presented.

The present disclosure may be provided as a computer program product, orsoftware, that may include a non-transitory machine-readable mediumhaving stored thereon instructions, which may be used to program acomputer system (or other electronic devices) to perform a processaccording to the present disclosure. A non-transitory machine-readablemedium includes any mechanism for storing information in a form (e.g.,software, processing application) readable by a machine (e.g., acomputer). The non-transitory machine-readable medium may take the formof, but is not limited to, a magnetic storage medium (e.g., floppydiskette, video cassette, and so on); optical storage medium (e.g.,CD-ROM); magneto-optical storage medium; read only memory (ROM); randomaccess memory (RAM); erasable programmable memory (e.g., EPROM andEEPROM); flash memory; and so on. By means of example and notlimitation, FIG. 7 provides a block diagram of a computer system 700 forproviding a visual display of spray patterns, according to certainimplementations. The system 700 includes a spray analysis andvisualization tool 710 with a database 711, a processor 712, a display713 and an input device 714 (e.g. a keyboard or remote control). In someimplementations, the spray analysis and visualization tool 710 may beone or more general purpose computers, special purpose computers orboth. In some aspects, the system 100 may be communicatively coupled toa communications network 715 for enabling a number of user devices 716to enter user input and receive information on the predicted sprayperformance of the selected from the system 700.

It is believed that the present disclosure and many of its attendantadvantages will be understood by the foregoing description, and it willbe apparent that various changes may be made in the form, constructionand arrangement of the components without departing from the disclosedsubject matter. The form described is merely explanatory, and it is theintention of the following claims to encompass and include such changes.

While the present disclosure has been described with reference tovarious embodiments, it will be understood that these embodiments areillustrative and that the scope of the disclosure is not limited tothem. These and other variations, modifications, additions, andimprovements may fall within the scope of the disclosure as defined inthe claims that follow.

What is claimed is:
 1. A computer-implemented method for depicting acomparison of agricultural spray, the method comprising: using acomputer processor configured to: receive at least two sets ofselections, each set comprising: a selection of an agricultural spray;and a selection of a spray parameter at which the agricultural spray isto be sprayed; retrieve analyzed spray particulate data using thereceived at least two sets of selections, the retrieved data for each ofthe sets of selections comprising a distribution of relative amounts ofagricultural spray droplets within droplet size classes where each classcorresponds to a range of droplet sizes; and transmit for graphicaldisplay relative amounts of the droplets in the droplet size classes forthe sets of selections, the relative amounts being relative volumes ofthe droplets in the droplet size classes and displayed as a volumetriccomparison of the sets of selections.
 2. The method of claim 1, whereinthe spray parameter comprises one or more of a spray rate, a spraypressure, or a nozzle type.
 3. The method of claim 1, wherein theagricultural spray comprises one or more of an active ingredient or anadjuvant.
 4. The method of claim 1, wherein the at least two sets ofselections further comprises a selection of an environmental parameter.5. The method of claim 4, wherein the environmental parameter comprisesone or more of boom height or wind speed.
 6. The method of claim 1,wherein at least one selection of the agricultural spray or theenvironmental parameter differs among the two sets of selections.
 7. Themethod of claim 1, wherein the computer processor uses the volumetriccomparison to characterize the agricultural sprays as acceptable orunacceptable or ranks the agricultural sprays relative to one another.8. The method of claim 1, wherein the volumetric comparison of the setsof selections is displayed on a per volumetric unit basis.
 9. The methodof claim 8, wherein the per volumetric unit basis is a per gallon basis.10. A computer-implemented method for depicting a comparison ofagricultural spray, the method comprising: using a computer processorconfigured to: receive at least two sets of selections, each setcomprising: a selection of an agricultural spray; and a selection of aspray parameter at which the agricultural spray is to be sprayed;retrieve analyzed spray particulate data using the received at least twosets of selections, the retrieved data for each of the sets ofselections comprising a distribution of relative amounts of agriculturalspray droplets within droplet size classes where each class correspondsto a range of droplet sizes; and transmit for graphical display relativeamounts of the droplets in the droplet size classes for the sets ofselections, wherein the relative amounts of the droplets within thedroplet size classes for the sets of selections are simultaneouslydisplayed as a cumulative distribution chart.
 11. The method of claim10, wherein the spray parameter comprises one or more of a spray rate, aspray pressure, or a nozzle type.
 12. The method of claim 10, whereinthe agricultural spray comprises one or more of an active ingredient oran adjuvant.
 13. The method of claim 10, wherein the at least two setsof selections further comprises a selection of an environmentalparameter.
 14. The method of claim 13, wherein the environmentalparameter comprises one or more of boom height or wind speed.
 15. Themethod of claim 10, wherein at least one selection of the agriculturalspray or the environmental parameter differs among the two sets ofselections.